xlr-preamp/preamp sim.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "9943dcb3",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"import PySpice.Logging.Logging as Logging\n",
"logger = Logging.setup_logging()\n",
"\n",
"\n",
"from PySpice.Probe.Plot import plot\n",
"from PySpice.Spice.Library import SpiceLibrary\n",
"from PySpice.Spice.Netlist import Circuit\n",
"from PySpice.Unit import *\n",
"\n",
"#spice_library = SpiceLibrary(\"library/\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "cf01c009",
"metadata": {},
"outputs": [],
"source": [
"circuit = Circuit(\"JFET preamp\")\n",
"\n",
"v_phantom = circuit.V('phantom_power', 'vdd', circuit.gnd, 48@u_V)\n",
"r_l = circuit.R('1', 'vdd', 1, 6.81@u_kOhm)\n",
"r_r = circuit.R('2', 'vdd', 2, 6.81@u_kOhm)\n",
"r_load = circuit.R('load', 1, 2, 3@u_kOhm)\n",
"\n",
"circuit.model('LSK489A', 'NJF', beta=2.2e-3, betatce=-.5, rd=11, rs=30, lambda_=4.3e-3, vto=-1.13, vtotc=-2.5e-3,\n",
" is_=3e-15,\n",
" isr=0, n=1, xti=0, alpha=30e-6, VK=120, Cgd=3.19e-12, Mj=0.32,\n",
" Pb=0.8, Fc=0.5, Cgs=2.92e-12, Kf=0.0009e-15,\n",
" Af=1, Gdsnoi=2.15, Nlev=3, Mfg='Linear_Systems')\n",
"\n",
"q1 = circuit.JFET('q1', 1, 'g1', 3, model='LSK489A')\n",
"q2 = circuit.JFET('q2', 2, 'g2', 4, model='LSK489A')\n",
"\n",
"rs1 = circuit.R('s1', 3, 5, 10@u_Ohm)\n",
"rs2 = circuit.R('s2', 4, 5, 10@u_Ohm)\n",
"\n",
"rs = circuit.R('s', 5, circuit.gnd, 5@u_kOhm)\n",
"\n",
"# gate biasing\n",
"rg1 = circuit.R('g1', 1, 'g1', 10@u_MOhm)\n",
"rg2 = circuit.R('g2', 2, 'g2', 10@u_MOhm)\n",
"rg3 = circuit.R('g3', circuit.gnd, 'g1', 10@u_MOhm)\n",
"rg4 = circuit.R('g4', circuit.gnd, 'g2', 10@u_MOhm)\n",
"\n",
"cg1 = circuit.C('1', 'g1', 'cin1', 100@u_uF)\n",
"cg2 = circuit.C('2', 'g2', 'cin2', 100@u_uF)\n",
"\n",
"rin1 = circuit.R('in1', 'cin1', 'in1', 0.5@u_MOhm)\n",
"rin2 = circuit.R('in2', 'cin2', 'in2', 0.5@u_MOhm)\n",
"vs = circuit.SinusoidalVoltageSource('input', 'in1', 'in2', amplitude=0.1@u_V, frequency=1e+3)\n",
"rt = circuit.R('tmp', 'in1', circuit.gnd, 5@u_MOhm)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "5b07a8e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<PySpice.Probe.WaveForm.OperatingPoint at 0x7fb5cf034850>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simulator = circuit.simulator(temperature=25, nominal_temperature=25)\n",
"\n",
"#analysis = simulator.transient(step_time=1@u_us, end_time=1000@u_us)\n",
"analysis = simulator.operating_point()\n",
"analysis"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "cd57b8c2",
"metadata": {},
"outputs": [],
"source": [
"tran_analysis = simulator.transient(step_time=1@u_us, end_time=1000@u_us)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "c48c200d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(WaveForm g1 [17.85236544]@V, WaveForm [0.00360743]@A)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analysis['g1'], analysis['5']/rs.resistance"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "32f62881",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb5cee1b6a0>]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hRTNGtMfPw41X1yaYHYoQprFK4ldKjVNKJSilkpRST1Txemel1FalVLFSao41yrSllOwCvtiRwqT+YYQ19TE7HGFFQb4eTB/ejnXxGew6cdbscIQwRb0Tv1LKFVgIjAe6ArcqpbpecFg28BDwan3Ls4d56xNxdVHMHiWt/cbo3qGRBPt58MqP0uoXzskaG8UOAJK01skASqkvgIlA/O8HaK0zgUyl1FVWKM+mkjLzWbE7lXuHRNLC38vscOpOaziXAdlH4dwpOJdlPD6XASXnoLQQSgugtAgqysDVHVzcjJubJ3gFgncQ+DQ1fgaEQmC4cfNq2COcfD3dmHl5B55fFc9vR04zuL3MzxDOxRqJvw2QUulxKjCwridTSk0HpgOEh4fXL7I6eH1dIt7urjxweXu7l11n509D+h44uRsy4uDMEchONhJ8ZcoFfJuDpz+4e4G7D3j4gHI1kn9FOZQVQ1EOZCVAYQ4U5/65PK8AaBYFLbpVunUH70Db19VKpgwMZ/HmI8xbl8igds1k+0zhVKyR+Kv6H1PnBdC11kuAJQDR0dF2XUg9Lj2X7/efZPaoDjTz87Rn0TWntZGUj22BY79Aagzkpf7v9cC2EBwFbQdD0/bQtB34twLfEKP17lLLBebKy6AwG3JTIeeE5XbciOHgSti11HKggpAuEDbQuLUdBEER1qq11Xm5u/LgyA78v2/j+O3IGYZ0kFa/cB7WSPypQFilx6FAuhXOa3evrT2Mv5cb04a1MzuUPzp/Gg6vgcS1RrIvsOwq5R8K4ZdB697Qqje06mn9bhhXN/ALMW5t+v7xNa0h/5TxLSN9N6RsgwPLIfYD4/Wm7aDDGOMWMRQ8fK0bWz1N6h/G25uO8Nq6wwxuL61+4Tyskfh3AlFKqUggDZgMTLHCee1q94mzbDiUyWNXdnKMTVZOJ8GhVZDwA6RsBzT4tYQOoyFimJFIgyLAzGSllPFtwr8VRI0xnqsoh6xDxgdU0gbY/QnsWAKuHhA5HLpeB52vMr59mMzTzWj1P/3NAbYknmZ4x+ZmhySEXSit69+bopSaAMwDXIH3tdb/VErNANBaL1JKtQRiAH+gAjgHdNVa513qvNHR0TomJqbe8dXE3R/sYG9KDr88PgpfT2t8HtZB/ik48DXsWwYn9xjPtewJnSZAp3FGq76htUpLi+DEVkhab3QN5ZwwLiBHjoBu10PXa029WFxSVsHIVzfRvIknK2YOlla/aPCUUrFa6+hLHmONxG8r9kr8e1JyuG7hr8wd18n+e+mWFkL8t7D3czi6GXSFkeB73AxdJ0JgWLWnaDC0NrqE4r+BuG+MawVu3kY9+9wGbYeCi/3nFH6+4wRPLt/PB/f0Z2SnELuXL4Q1SeKvoXs+2MGelBy2PD4KP3u19rMOG33hez4zRtEERUCPW4yE39wJ1gbSGtJija6gA19DcZ5xYbrP7dD3LmjSwm6hlJZXMOo/mwjy8eDbB4dIq180aDVJ/Cb1aTiOPSk5bEzI4rErO9k+6ZeXwaHvYMe7cPwXcHGHLtdA9D1Gv70zJRylIDTauF35onE9Y/cnsPGf8PPL0P0GGHg/tOln81DcXV2YPTKKuV/v46dDmYzuYr8PHSHM4PQt/ns/3MmuE2f5xZat/eJzsOdT2LrQ6N4IbAv97jZat37StfAHZ44YF4N3fwol+RDaHwbOMLqDXG130b20vIIxr/2Mn6cbq2YPlVa/aLBq0uJ36kXa9qbk8NOhTO4b1s42ST//FGx4Hl7vBj/MhSYt4ZaP4aHdMOyvkvSr0qw9jP83/DUexv0bCs7A11Phjb6w8z3jYrENuLu6MHtUFHHpeayNz7BJGUI4Cqdu8U/9cCexJ86yZe5ImnhZsTWZmwa/vA67PoLyEqM7Z/BsCBtgvTKcRUUFHP4RtvwH0mLArwUMmmV0j3k2sWpRZeUVjH19M17urnw/e6jsrywaJGnxX8K+1Bw2HMpk2tBI6yX93FT4/lFY0Nu4cNtrMjy0CyZ9LEm/rlxcoPMEmLYe7lwJzTvDumfg9e6w6d9QdMkRwbXi5urCw6OjOHgyjzVxp6x2XiEcjdMm/gUbEgnwdueuwRH1P1luKqz6KyzoA7FLofcUmL0Lrl1gzF4V9acUtBsBd62EaRuMJSk2vQjze8GvC4xhsVZwTa/WtG/uy/wNiVRUOO63YSHqwykT//7UXNYftEJrvyAb1vwNFvQ1unV632a08K+ZD0FtrRew+KPQaLj1c7hvI7TuY3wDmN8bdrwDZSX1OvXvy3EfOpXPuoPS1y8aJ6dM/PN/b+0PiajbCUoLjT78+b2NkTo9brIk/HnGssXCPtr0hTuWw92rjW9Wq+fAG/1g7xfGtYE6urpnKyKDfVmwIRFHvgYmRF05XeI/kJbL+oMZTB0aiX9tW/sV5bDrY6OFv/5ZY4G0B36F696ShG+miCFwz2q4/WtjDaAV98M7I431gurAzdWFB0d2IC49j58OZVo5WCHM53SJf/6GRPy93Li7tq39pPXw9hBYOQv8W8Pd38Nty4y16IX5lDJWAb1vI1y/xFjR9MOr4IvbjAXvamli79aEN/WRVr9olJwq8R9Iy2VdfAZTh7areWv/zBH4bBJ8ciOUFcEtHxkjTCKG2jZYUTcuLtBrEsyOgVHPQPImeGsg/PC4cU2mhtxdXXhwZHv2puby8+Es28UrhAmcKvEv2JBIk5q29ovyYO0zsHAgHPsVxj4PD243ZpDKrE7H5+4Nw+cYk+X63GHMBl7QG7YvNpbOqIHr+4TSJtCb+dLqF42M0yT+uPRc1sYbffuXXG+/osLox3+jL/z2hqX1GAtDHjb2ohUNi1+IcdH9gd+gdV9jBvXi4caHeTU83FyYObI9u0/k8GvSGdvHKoSdOE3i/721f8+QyIsfdGK7cVFw5SxjlMh9P8HEhXZdKVLYSEgXuGMFTPoEivPhwwnw1VTIu/RmcTf1C6VVgBfzNxyWVr9oNJwi8cen57EmLoN7h1yktX/+DHz7ILx/BZzLhBvehXvX/HmrQdGwKWUsn/HgdhjxOBz8Dt6Ihl/mXXT8v6ebKzNGtGfnsbNsS675NQIhHJlTJP4FGxJp4unGvRe29isqjIlXb1rGfg95GGbthJ43Sz9+Y+bhAyOfMj4A2l0O6/8Obw8yRm5VYVL/MEKaeLJgQ6J94xTCRhp94o9Pz+PHuFPcMySCAJ9Krf1TB+CDcbByNjTvAvdvMS7gevqZF6ywr6aRcOtncNtXxsYwn9wIy+76U/ePl7sr949oz9bkM+w4Kq1+0fA1+sQ/f8Nhmni5MXWoZc2c4nxjmYXFw+FMEkx8y5j806KruYEK80SNhZlbYdTTxkqgbw6AbYv+MPpnyoBwgv08eOMnafWLhq9RJ/649NxKfftuxt62bw6ArW8am6DMijH2epVuHeHmCcMfMz4AwgbAj4/Du6OM7SEBbw9Xpg9vx5bE08QeP2tysELUT6NO/PPXGyN5pnZX8NktsOxO8GkGU9cZK2f6NDU7ROFomrYzln64+UPIz4B3RsP3c6Aol9sGtqWpr7T6RcNnlcSvlBqnlEpQSiUppZ6o4nWllFpgeX2fUsrmw2UOpOWyKT6VReE/4f/eUDj+m7G36/RNsja+uDSloNv1xoX+AdMh5j14sz++h79h2tAINiVksTclx+wohaizeid+pZQrsBAYD3QFblVKXdhhPh6IstymA2/Xt9xLqajQfPnVp6zxepIhJxZBx3HGf+JBD4Kr0+8vL2rKyx8mvGys/9+kFXw9lenH59DT+7S0+kWDZo0W/wAgSWudrLUuAb4AJl5wzETgI23YBgQqpVpZoew/KTp3lu3/uYHnzj5JsI8r3PY13LLUWFhNiLpo09eYzDf+FdzSY1nOHLoeXkTcCVm5U1hZRbmxr8Ty+21ajDUSfxsgpdLjVMtztT0GAKXUdKVUjFIqJiur9otjeXr70bQ4nW1h0/B7ZAdEjan1OYT4ExdXGDgdZu1Ed57AX92/otnHI41F4ISwhvTd8O5oWD2HnKxUdPE5mxVljcRf1ZCYC+e21+QY40mtl2ito7XW0c2bN699MK7udHrqNy6b+h+Uh0+t3y/EJfm3wn3SUpZ3XUBRcSl8NNFY+iFf9ugVdVSUC6vnwjujIC+dhc2eZEzmwxS7eNusSGsk/lQgrNLjUODCBVBqcoz1uLja7NRCAIy+ego3qv/wfdM74eBKeLN/rVb+FAKt4cByY4j5jiXQfxp7rlvHK2k9uG94e7zcbZfHrJH4dwJRSqlIpZQHMBlYecExK4E7LaN7LgNytdYnrVC2EKYI8HHn1sEdeTB9HEdvWQ9t+hkrf74zElJjzA5POLrsZGOm+Ff3GItA3vcTTHiF+VsyCPJx5/bLbLtnd70Tv9a6DJgFrAEOAsu01nFKqRlKqRmWw1YDyUAS8A4ws77lCmG2qUMj8fVw5T+x5cbKnzd9YCzy9+4Y+O6RWm38IpxEWTH8/DIsvAxSdsD4l41d49r0ZV9qDhsTspg2rB2+nrYdfWiVs2utV2Mk98rPLap0XwMPWqMsIRxFkK8Hdw6OYNHPR3h4dBRR3W8wtn/c9C/YvshY/fOKF6DXrTI7XMDRzbDqr3AmEbrdYMwr8v/f4MYFGxIJ8HbnzkG2be1DI5+5K4St3TesHd7urrzxk2VfXy9/GPcvmP6zMQv4mwfggwmQEW9uoMI8uWnw1b2w9BqoKLPMDP/gD0n/QFou6w9mMnVoJE1qui1sPUjiF6Iemvp6cMegtny3L52kzErD71r1NPZ0uGYBZB2ExcOMrTxtOERPOJiyYtjymnHh/9D3MOIJYy2oDn8eYr5gQyL+Nd0W1gok8QtRT9OHtcPLzZU3L5zN6+IC/e6CWbHQazL8tgAWDoADXxsjOkTjlbge3hoEG56D9iPhwR0w8kljL+gLxKfnsTY+g3uHRuJvh9Y+SOIXot6a+Xlyx6C2rNybTnJWFS1632bGFp73rjEWBvzqXvhgvDFhRzQu2Ufh81vh0xuN6zq3fw2TP4Wgi/fbv/GTsVHUPYMvsS2slUniF8IK7hvWDg83F978va+/KuGXGX3/18yH04mwZCR8O8sYCSQatpLz8NM/YeFASP4ZxjwHD1TdrVPZoVN5/HCgio2ibEwSvxBW0LyJJ7cPbMs3e9I4evr8xQ90cYV+d8NDu4xFA/d+AQv6wq8LLrrvr3BgFeWw62Pjd7j5ZehyNcyOgaGPgJtHtW9/Y0MSfp5u3DvUfq19kMQvhNVMH9EOd1cXFm68RKv/d14BcOU/YeY2iBgC656BtwYaFwGl/79hOLIRFo+AlbMgMAzuXQs3vV/jBSEPZ+Sz+sBJ7hrclkCf6j8krEkSvxBWEtLEi9sGtmXF7jSOn7lEq7+y4A4w5b/GKrIubvDFFHh/HJzYZttgRd1lJcCnt8DH10FxrpHsp66D8IG1Os0bPyXh4+7KtN+3hbUjSfxCWNGMEe1wc1E1a/VXFjXG6BO+eh6cPQbvXwmfT4HMQ7YIU9RFXroxI/utQXBiK4x9Hh7cCd1vrPUEvaTMfFbtS+fOwREE+dq3tQ+S+IWwqhB/L24dEM7yXWmkZBfU7s2ubhB9j9H/P+oZOLYF3h5kXADOTbNNwKJ650/Dmr/B/N6w+xPoPxUe2g1DHgZ3rzqd8s2fkvByc2Wanfv2fyeJXwgre+Dy9rjUpdX/Ow9fGD4HHtoDAx+Aff+FBX2MpXvzbLeorbhAYQ789A+Y3wu2vQU9boLZsTDhFfANrvNpk7POsXJvOncOakszP0/rxVsLkviFsLIW/l7c2j+Mr2JTa9/qr8y3GYx7EWbFQM+bYee7Rqvz+znyDcCWCs/Cz68YCX/zK8aQzJnb4bq3Ljkev6be3JiEh5sL9w23f9/+7yTxC2EDMy5vj4tSvLXpSP1PFtTWmAD20C7oNQliP4AFvY0Fv3JSqn27qKFzmbDu7/B6D9j4D2Pexf2bja1bm3e0ShHHTp/n2z3p3D6wLcEmtfZBEr8QNtEqwJtJ/cP4KjaFtJxC65w0KAKufQNm74LeU2DXR8YHwNf3wcm91inDGeWcML5FzethLKvR8QqY8Ysx2qpVL6sW9ebGJNxcFNNHmNfaB0n8QtjMA5e3B+Ctuvb1X0xQW2P270O7YcD9kLAaFg83Vn9MXCfzAGpCazi+FZbdaXSfxX4IPW8xutVueh9a9rB6kSfOFLBidxq3DWxLSJO6XRS2Ftuu9i+EE2sd6M0t0WEsi0nhwZEdaB1o5T1UA8OMawAj5sKupbBtEXx6EwR3Mkae9JwE3oHWLbOhKy0yFsnbvghO7QOvQBg8y/gADWhj06Lf3JiIq4vifpNb+yAtfiFsaubIDgC8bY2+/ovxDjSGFj68F65fYowK+mEu/KczfPOgsRWks38LyIiHH5+C17vCtzONdfGvmQ9/PWiMx7dx0j92+jxf70rjtoHhtPA3t7UP0uIXwqbaBHpzU78w/rszhZkj29MqwMqt/srcPIyLv70mQfoe4yLwvi9hzyfQorvRldH9JpsnOYdRmAMHvjLG3qfvBhd36DQe+k+DyOF23RVtwYZE3F3V/3X/mU1pB24JREdH65gY2bhaNGwp2QWMfHUTUwaG8/zE7vYtvCgP9n8Jez6DtBhAQdshxvDQLtcay0Q3JkW5kPAjxH8DSeuhvMT40OtzO/S4xRgia2dJmee44vWfmTasHU9N6GLz8pRSsVrr6EsdIy1+IWwsrKkPt/QP4/MdJ5g+vB2hQT72K9zL3+jv7z8VzhyB/V/B/mXw3cPGcNC2g41WcMdx0MwxWqO1lnfSSPKHvocjG4xk79/GaNn3nGSMzDFxz+N56w/j5e7K/SaO27+QtPiFsIOTuYWMeGUTN/Rpw0s39jQ3GK2Nro9DqyDhB8i07Acc3MnYLSpiqPGtwFG/DZQUQOoOSNpg3DLjjOf9Q6HrROh2HbSJNnZAM9mhU3mMn7+FmZe357ErO9ulTJu3+JVSTYH/AhHAMeAWrfXZKo57H7gayNRa2/m7rhDmaxXgzW0Dw/lo63HuH9GeyGBf84JRCtr0NW6j/5+xKFzCj3D4R4hdaox4QRldJG0HQes+0Ko3BHc01hOyp4oKyDkOJ/fAie2Qst0YjVNRZvTZtx1kXJztMAZCuprasq/KvHWJ+Hm4cd8wx2ntQz1b/Eqpl4FsrfVLSqkngCCt9eNVHDccOAd8VJvELy1+0Zhk5hcx4uVNXNmtBfMm9zE7nKqVlUD6Lji6xVgkLjUGSi1LTLt5Q4tuxgdAs3bQtL3RPeQfCt5BdW9haw1FOcZEqpwTcPY4nD5sfBPJPAgl5/5Xfpt+xvLHYQONbyWeflapti0cSMvl6jd+4eHRUfxlrHVm/tZETVr89U38CcDlWuuTSqlWwCatdaeLHBsBrJLEL5zZSz8cYvHmI6x5ZDgdWzQxO5zqVZTDmSRjlNDJPXBqv/E4/+Qfj1Ou4Nsc/EKM4aXuPuDmZfx0dTfOU1EGFaVGH3xRLhScNdbFKcyG0gvWNPIOMr5xhHQ1PmxadocWPWq0q5WjmLZ0JzuOZrPl8VEEeNtvW0V7XNxtobU+CWBJ/iH1PB9KqenAdIDw8PD6nk4Ih3L/8HZ8su04r687zNu39zM7nOq5uELzTsat16T/PV9yHrKTjVv+KTiXYbllGiOJCnOgrAhKC41E7+JunMvFzfgg8AqEgFBjhqx3kLFrVWD4/27eQQ7XbVMbe1JyWH8wkzlXdLRr0q+pahO/Umo90LKKl/5m/XBAa70EWAJGi98WZQhhliBfD6YOjWT+hkQOpOXSvU2A2SHVjYevkbRtsLRBY/D6usME+bhz9xBz1tuvTrWdclrrMVrr7lXcvgUyLF08WH5m2jpgIRq6qcMiCfB257V1h80ORdhA7PFsfj6cxf0j2uPn6Zgj5us73mklcJfl/l3At/U8nxCNnr+XO/ePaMdPhzKJPf6nQXCiAdNa88qaBIL9PLhzUP3X7reV+ib+l4CxSqlEYKzlMUqp1kqp1b8fpJT6HNgKdFJKpSqlptazXCEatLsHRxDs58F/1iaYHYqwos2Jp9mWnM2skR3w8XDM1j7U8+Ku1voMMLqK59OBCZUe31qfcoRobHw83Jh5eQeeXxXPb0mnGdyh7lv5CcdQUaF5+cdDhAZ5M2Wg47b2QVbnFMI0UwaG09Lfi1fXJuDIM+hFzXy//yRx6Xk8ekVHPNwcO7U6dnRCNGJe7q7MHt2BXSeMoX+i4Sotr+A/axPo3LIJ1/Zy/NVPJfELYaJbosNoF+zLv388RFl5hdnhiDpaFpPCsTMFPHZlJ1xdHH/+gSR+IUzk7urC3HGdSMo8x9e7Us0OR9RBYUk589cnEt02iFGd6z2H1S4k8Qthsiu7taRPeCCvrTtMYUm52eGIWvrwt2Nk5hfz+PjOqAYy21gSvxAmU0rx5PguZOQV8/6vR80OR9RCbkEpb29KYnTnEPpHOOgy1lWQxC+EAxgQ2ZQxXUJYtOkIZ8+XmB2OqKG3fz5CfnEZc66scm1KhyWJXwgHMXdcZ86XlPHmxiSzQxE1kJ5TyAe/HuW63m3o0srf7HBqRRK/EA6iY4sm3NQvlI+3Hiclu6D6NwhTvbLGmHXd0Fr7IIlfCIfyl7EdUQpZwM3B7UvNYcXuNKYOjaRNoLfZ4dSaJH4hHEirAG/uGRLJN3vSOJCWa3Y4ogpaa/7x/UGa+XrwwOUNc4N6SfxCOJgHLm9PkI8Hz6+Kl6UcHNDa+Ax2HM3mkbEdaeLleJus1IQkfiEcTIC3O38d25EdR7P58cAps8MRlZSUVfDSD4foEOLHrf3DzA6nziTxC+GAJvcPo1OLJrz4w0GKSmVSl6P4dPtxjp4+z1MTOuPm2nDTZ8ONXIhGzM3Vhaev7kJKdiEf/HrM7HAExmSt+RsSGdKhGSM7NYylGS5GEr8QDmpYVHPGdAlh4cYkMvOLzA7H6c3bcJjcwlKemtClwSzNcDGS+IVwYH+7qivFZeW8tlaGd5op4VQ+H209zuT+4XRrHWB2OPUmiV8IBxYZ7MtdgyL4b0wKcekyvNMMWmueXRmHn6cbjzXAyVpVkcQvhIObPTqKQG93nlspwzvNsHr/KbYmn+HRKzrS1NfD7HCsQhK/EA4uwNudueM6s+NYNit2p5kdjlMpKCnjn9/H07llE6YMCDc7HKuRxC9EAzApOozeYYG8uPoguYWlZofjNN7edIT03CKeu7Zbgx6+eaF61UQp1VQptU4plWj5GVTFMWFKqY1KqYNKqTil1MP1KVMIZ+TiovjHdd3JPl/Ca2sTzA7HKZw4U8Dizclc26s1A9s1Mzscq6rvR9gTwAatdRSwwfL4QmXAo1rrLsBlwINKqa71LFcIp9O9TQC3X9aWj7cdl3V8bExrzXPfxeHmonhqQhezw7G6+ib+icBSy/2lwHUXHqC1Pqm13mW5nw8cBBx/G3ohHNCjV3Siqa8HT39zgIoKudBrKz8cOMWGQ5n8ZUxHWgZ4mR2O1dU38bfQWp8EI8EDl5zOppSKAPoA2y9xzHSlVIxSKiYrK6ue4QnRuAR4u/PUhC7sSclhWUyK2eE0SnlFpTy7Mo5urf25Z0iE2eHYRLWJXym1Xil1oIrbxNoUpJTyA74GHtFa513sOK31Eq11tNY6unnz5rUpQgincH2fNgyIbMpLPx7i9Llis8NpdF75MYHT54r51w09GtUF3cqqrZXWeozWunsVt2+BDKVUKwDLz8yqzqGUcsdI+p9qrZdbswJCOBulFC9e352C4nKeXRlndjiNSuzxs3yy/Th3DY6gZ2ig2eHYTH0/zlYCd1nu3wV8e+EByljU4j3goNb6tXqWJ4QAOoQ0YfaoDqzad5K1cbJ0szWUllfwtxX7aenvxaNXNI4ZuhdT38T/EjBWKZUIjLU8RinVWim12nLMEOAOYJRSao/lNqGe5Qrh9O4f0Z7OLZvwzLcHZGy/FbyzJZlDp/J57tpu+Hm6mR2OTdUr8Wutz2itR2utoyw/sy3Pp2utJ1ju/6K1Vlrrnlrr3pbb6kufWQhRHQ83F16+qSdZ+cW89MNBs8Np0BIz8pm3LpHx3VtyRbeWZodjc43zyoUQTqJnaCD3DWvH5ztS+C3ptNnhNEhl5RU8+uVe/LzceOG67maHYxeS+IVo4B4Z05GIZj48sXw/54vLzA6nwVm8OZl9qbm8MLE7wX6eZodjF5L4hWjgvD1cefmmXqScLeCFVfFmh9OgHDqVx7z1h7m6Zyuu6tnK7HDsRhK/EI3AgMim3D+8PV/sTGFdfIbZ4TQIpeUVPLpsLwHe7jw/0Tm6eH4niV+IRuIvY6Po0sqfJ77eR1a+TOyqzuvrDhOXnsc/ruvRaNbZrylJ/EI0Ep5ursyf3Jv84jKe+HqfbNpyCb8lnebtn48wKTqMcd0b/yieC0niF6IR6diiCY+P68yGQ5l8tuOE2eE4pOzzJfxl2R4ig335+7XOuVCwJH4hGpl7BkcwLCqY57+LJz79ostiOSWtNXO/2sfZ86UsmNwHH4/GPVHrYiTxC9HIuLgoXp/Um0Afdx78bBf5RTKr93cfbzvO+oMZzB3Xie5tAswOxzSS+IVohIL9PFkwuQ8nsgt4cvl+6e/HWIDthVXxjOocwr1DIs0Ox1SS+IVopAa2a8ajV3Rk1b6TfLLtuNnhmCorv5iZn8bSKsCb12/pjYuLMjskU0niF6IRmzG8PSM7Nef5VfHEHMs2OxxTlJVXMOuzXeQUlLLo9n4E+LibHZLpJPEL0Yi5uCjmTepDaJAPMz6JJS2n0OyQ7O6lHw6x/Wg2/7qhB11b+5sdjkOQxC9EIxfg4847d0ZTXFrBtKUxFJQ4z3o+y3am8O4vR7lzUFtu6BtqdjgOQxK/EE6gQ4gfC6b0IeFUHo8u2+sUG7X/mnSap1bsZ1hUMM9c7Zzj9S9GEr8QTmJkpxCeHN+FHw6c4t8/HjI7HJtKysxnxiextGvuy8Lb+uLeSPfOrSvnnL0ghJOaNiySE9kFLN6cTPMmnkwb1s7skKwuI6+Iez7ciaebK+/d1R9/L7mYeyFJ/EI4EaUUz17bjTPni/nH9wcJ9vPkuj5tzA7LarLPl3D7u9vJPlfCZ/ddRlhTH7NDckiS+IVwMq6Wmb1nz+9kzpd78fN0Y0zXFmaHVW/5RaXc9f4OjmcXsPSeAfQKCzQ7JIclHV9COCFPN1eW3NmPbq39eeDT2Aa/hv/54jKmLo3h4Mk83r6tL4PaNzM7JIcmiV8IJ9XEy52Ppg6ka+sAZn4ay5q4U2aHVCe5haXc8d52Yo5l89qk3ozu0vC/vdhavRK/UqqpUmqdUirR8jOoimO8lFI7lFJ7lVJxSqnn6lOmEMJ6Arzd+XjqALq1DuDBT3excm+62SHVSvb5Eqa8s439abm8dVtfru3V2uyQGoT6tvifADZoraOADZbHFyoGRmmtewG9gXFKqcvqWa4Qwkr8vYzk3zc8iIc+382SzUcaxKJux8+c5+ZFv5GUeY4ld0Yzrrvz7JlbX/VN/BOBpZb7S4HrLjxAG85ZHrpbbo7/VyWEEzG6fQZwVY9WvLj6EM99F09ZeYXZYV1UzLFsrn/rN86cL+GjewcwslOI2SE1KPUd1dNCa30SQGt9UilV5b++UsoViAU6AAu11tsvdkKl1HRgOkB4eHg9wxNC1JSXuytv3NqHlgFevPfLURJO5fPGlD4E+3maHdr/0VrzZWwqT684QJsgb96/uz+Rwb5mh9XgqOq+0iml1gNVbUr5N2Cp1jqw0rFntdZ/6uev9HogsAKYrbU+UF1w0dHROiYmprrDhBBW9mVMCk9/c4AgHw8W3taXfm0v+t/abs4Xl/HMNwdYvjuNwe2b8dZtfQn0ca5N0mtCKRWrtY6+1DHVtvi11mMuUUCGUqqVpbXfCsis5lw5SqlNwDig2sQvhDDHzdFhdG3tz4xPYrll8VZmjGjHQ6Oj8HRzNSWePSk5PLpsD8mnz/PImChmj4rC1cnX1K+P+vbxrwTusty/C/j2wgOUUs0tLX2UUt7AGKBxLxQiRCPQrXUAq2YP44Y+bVi48QjXvvEre1Jy7BpDQUkZL6yK54a3fuV8cTmfThvII2M6StKvp2q7ei75ZqWaAcuAcOAEcLPWOlsp1Rp4V2s9QSnVE+PCryvGB80yrfXzNTm/dPUI4Rh+OpTBE1/vJzO/mBv6tOGxcZ1oFeBts/LKKzRfx6by2rrDnMor4vbLwpk7rrOsu1MDNenqqVfitzVJ/EI4jnPFZby1MYl3fzmKAm6ODmX6sPaEN7PeejglZRWs3JvOks1HOJxxjt5hgTx9VReiI5parYzGThK/EMLqUrILeGtTEl/HplFWUcHITiHc0DeU0V1C8HKv2zWAQ6fy+G5vOl/GpJKZX0zHFn48MqYj47u3RCnp1qkNSfxCCJvJyCviw9+OsXxXKhl5xfh4uDIwsilDOgTTtbU/HVs0oZmvx58Sd1FpOcfPFHDoVB47jmazNfkMyVnncXVRDIsK5t4hkQyLCpaEX0eS+IUQNldeofk16TTrD2bwS+Jpkk+f/7/XvNxd8Pdyx9fTjZKyCgpKyjhbUPp/r/t5utGvbRBjuoQwvkcrh5oz0FBZZTinEEJciquLYnjH5gzv2BwwvgkczsjncMY5MvKKyCss5VxxGV7urni5uxDSxIu2zXxo39yPzi2b4Ca7Y9mdJH4hhFW18Peihb8Xw6Kamx2KuAj5qBVCCCcjiV8IIZyMJH4hhHAykviFEMLJSOIXQggnI4lfCCGcjCR+IYRwMpL4hRDCyTj0kg1KqSzgeB3fHgyctmI4DYHUufFztvqC1Lm22mqtLzl7zqETf30opWKqW6+isZE6N37OVl+QOtuCdPUIIYSTkcQvhBBOpjEn/iVmB2ACqXPj52z1Bamz1TXaPn4hhBBVa8wtfiGEEFWQxC+EEE7GYRO/UmqcUipBKZWklHqiiteVUmqB5fV9Sqm+1b1XKdVUKbVOKZVo+RlU6bUnLccnKKWutH0N/8yedVZKjVVKxSql9lt+jrJPLf9UJ7v+ni2vhyulziml5ti2dn9mwt91T6XUVqVUnOV37WX7Wv6pTvb8u3ZXSi211PWgUupJ+9TyT3WyRZ1vtvweK5RS0Recr3b5S2vtcDfAFTgCtAM8gL1A1wuOmQD8ACjgMmB7de8FXgaesNx/Avi35X5Xy3GeQKTl/a6NvM59gNaW+92BtMb+e650zq+BL4E5jbm+GDvs7QN6WR43c4K/6ynAF5b7PsAxIKKR1LkL0AnYBERXOlet85ejtvgHAEla62StdQnwBTDxgmMmAh9pwzYgUCnVqpr3TgSWWu4vBa6r9PwXWutirfVRIMlyHnuya5211ru11umW5+MAL6WUvXe6tvfvGaXUdUAyRp3tzd71vQLYp7XeC6C1PqO1LrdR3S7G3nXWgK9Syg3wBkqAPNtU7aJsUmet9UGtdUIV5dU6fzlq4m8DpFR6nGp5ribHXOq9LbTWJwEsP0NqUZ6t2bvOld0I7NZaF9c5+rqxa52VUr7A48BzVoq/tuz9O+4IaKXUGqXULqXUXKvUonbsXeevgPPASeAE8KrWOrv+1agVW9W5PuX9gaNutq6qeO7CcacXO6Ym761LebZm7zobJ1SqG/BvjNahvdm7zs8Br2utzylV1dttzt71dQOGAv2BAmCDUipWa72hukCtyN51HgCUA62BIGCLUmq91jq5ukCtyOHzl6Mm/lQgrNLjUCC9hsd4XOK9GUqpVlrrk5avVZm1KM/W7F1nlFKhwArgTq31EavUonbsXeeBwE1KqZeBQKBCKVWktX7TGpWpATP+rn/WWp8GUEqtBvoC9kz89q7zFOBHrXUpkKmU+hWIxujesxdb1bk+5f2RrS5w1OeG8YGUjHGh4vcLHN0uOOYq/nhxZEd17wVe4Y8XhF623O/GHy+OJGP/i2D2rnOg5bgbneX3fMF5n8X+F3ft/TsOAnZhXOR0A9YDVzXyOj8OfGA5ly8QD/RsDHWu9N5N/PHibq3zlyn/4Wv4jzcBOIxxhfpvludmADMs9xWw0PL6/gv+If70XsvzzTBaO4mWn00rvfY3y/EJwPjGXmfgaYy+0D2VbiGNuc4XlPssdk78Jv1d345xIfsAVXwANrY6A34YI7biMJL+Y42oztdjtO6LgQxgTaXXapW/ZMkGIYRwMo46qkcIIYSNSOIXQggnI4lfCCGcjCR+IYRwMpL4hRDCyUjiF0IIJyOJXwghnMz/B09elN8LsgtdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(tran_analysis.time, tran_analysis['2']-tran_analysis['1'])\n",
"plt.plot(tran_analysis.time, tran_analysis['in1']-tran_analysis['in2'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "ae8719b6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10.326951614171897"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"20*np.log10(float(np.std(tran_analysis['2']-tran_analysis['1'])/np.std(tran_analysis['in1']-tran_analysis['in2'])))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "56ee9649",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb5cee7ead0>]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(tran_analysis.time, tran_analysis.branches['vphantom_power'])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e5444251",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb5cecf5930>]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ShaKSUmYs3M6f3t1Ibn4R145O5vvj+mtlJ2lwFPQiFbg776/fy69fX8uWfYc5s18nfnbxIE7qqoeOScOkoBcpZ/3nufz69TV8tHE/vTvF88yNqYw9qbPG4aVBU9CLUDYO/+g765m5aAetm8fx4CWDmHR6T+JidEerNHwKemnUSkqd2Ut28Mhb68nNL2bSaT25Z1x/2sdrAW6JHgp6abTSt3/Bz+etYtWuQ4zu1YFfThiicXiJSuEsDt4cWAA0C7X/h7v/vEKb64Afh17mAVPcfXlo3zYgFygBiqta6kqkvuzPK+Dh+ev4e3omXds05y/XDOOSU7ppHF6iVjhn9AXAue6eZ2ZxwMdmNt/dF5ZrsxU4292/MLMLganA6HL7x7r7/torW6TmiktKeWHhdv7wzgbyi0q44+w+3HVuX+Kb6R+2Et2q/RvuZauH54VexoW+vEKbT8u9XAgk1VaBIrVh0ZYD/HzeatZ9nsuZ/Trx0KWD9VwaaTTCOpUxsxggHegLPO7ui47R/BZgfrnXDrxtZg485e5Tq/gZk4HJAMnJyeGUJVKtPYfy+e0ba/nnsiwS27XgyetHcMFgPbZAGpewgt7dS4BTzawdMNfMhrj7qortzGwsZUH/tXKbx7h7lpl1Bt4xs3XuvqCSnzGVsiEfUlNTveJ+kZooKXWmf7aNR9/eQGFJKd/7ej+mnN2HFk312AJpfGo0OOnuOWb2ATAe+I+gN7NTgKeBC939QLljskL/3Wtmc4FRlF3cFakTKzMP8tO5K1m56yBn9U/gVxMG07NjfNBliQQmnFk3CUBRKORbAOOAhyu0SQbmAJPcfUO57fFAE3fPDX1/PvDL2uyAyJdy84t49O0NTP9sGx1bNdNsGpGQcM7ouwHPh8bpmwAvu/trZnYHgLs/CTwIdASeCP1P9eU0yi6UDfV8+bNmuvubtd8NaczcnTdXfc5D/1rN3twCrh/dk/suOIm2LbSMnwiAlU2qiSypqamelpYWdBnSAOzMPsLP563m3+v2MrBbG357+RCGaREQaYTMLL2q+5Q0gVgapKKSUqZ9vJXH3t2IGfzs4oHceEYKsXo2jchXKOilwVmZeZAfvbKCtbsPcd6gLjx06WAS27UIuiyRiKWglwYjv6iEP767gac/2krH+KY8ef0Ixg/pGnRZIhFPQS8NwmebD/CTOSvYduAIE0f24CcXDdTFVpEwKegloh3KL+J3b6xj1uIdJHdoycxbR3NG305BlyXSoCjoJWK9s2YPP3t1JftyC7jtzF784LyTdGeryHFQ0EvEOZBXwM/nrea1FbsZ0LU1UyelMrRHu6DLEmmwFPQSUeav3M3PXl3FofwifnBef+44uw9NYzVlUuREKOglInxxuJAH563mX8uzODmxLTOvOk2rPYnUEgW9BO7t1Z/z07mrOHi0kHvP688d5/TRotwitUhBL4HJOVLIQ/NW8+qyLAZ1a8P0m0cxqHuboMsSiToKegnEe2v3cP+clXxxuJC7v96P747tq7F4kTqioJd6lVdQzK/+tYaX0nYyoGtrnr1xJEMS2wZdlkhUU9BLvUnbls0PXl5O5hdHmHJOH74/rr/O4kXqgYJe6lxhcSmPvbeBv32wmcT2LXjp9tMZmdIh6LJEGg0FvdSpjXtyueelZazOOsTVqUn81yWDaN1cz6gRqU8KeqkTpaXO859t4/fz1xHfLJanJo3ggsF60qRIEBT0Uuv25uZz78vL+Wjjfs4d0JmHrziFhNbNgi5LpNGq9kqYmTU3s8VmttzMVpvZLyppY2b2ZzPbZGYrzGx4uX3jzWx9aN/9td0BiSz/XreHC//0EUu2ZfPry4Yw7dupCnmRgIVzRl8AnOvueWYWB3xsZvPdfWG5NhcC/UJfo4G/AaNDC4o/DpwHZAJLzGyeu6+p1V5I4PKLSvj9/HU89+k2BnRtzexrTqNfFz3CQCQSVBv0XrZ6eF7oZVzoq+KK4hOA6aG2C82snZl1A1KATe6+BcDMZofaKuijyIY9uXxv1lLWfZ7LTWNS+PH4ATSP0+OERSJFWGP0oTPzdKAv8Li7L6rQJBHYWe51ZmhbZdtHV/EzJgOTAZKTk8MpSwLm7sxYtINfv7aGVs1iefbGkYwd0DnoskSkgrCC3t1LgFPNrB0w18yGuPuqck2sssOOsb2ynzEVmAqQmppaaRuJHAePFnH/KyuYv+pzzuzXiUevHkrn1s2DLktEKlGjWTfunmNmHwDjgfJBnwn0KPc6CcgCmlaxXRqw5TtzuHNWBrtz8vnJhQO47czeNGlS2e90EYkE4cy6SQidyWNmLYBxwLoKzeYBN4Rm35wGHHT33cASoJ+Z9TKzpsDEUFtpgNydaR9v5conP6W0FF6+43RuP7uPQl4kwoVzRt8NeD40Tt8EeNndXzOzOwDc/UngDeAiYBNwBLgptK/YzO4E3gJigGfcfXXtd0PqWs6RQu77+wreXbuH8wZ14f9dOZS2LXWHq0hDYGUTZSJLamqqp6WlBV2GhGTs+IK7Zi5lb24+P7lwIDeNScFMZ/EikcTM0t09tbJ9ujNWquTuPPfpNn7z+lq6tWvOK1PO4JSkdkGXJSI1pKCXSuUVFPPjV1bw+ordjBvYhUev0lCNSEOloJev2LAnlztmpLNt/2Huv3AAt5/VW0M1Ig2Ygl7+w9ylmfx0zirim8Uy87bTOK13x6BLEpETpKAXoGxxkF+/vobpn21nVK8O/PWaYXRuoxugRKKBgl7Yeyif77yYQdr2L7jtzF78ePwAYmO0xJ9ItFDQN3Lp27OZMiOD3Pxi/nzNMC4d2j3okkSklinoG6kvH0j2y3+tpnu7Fky/ZRQDurYJuiwRqQMK+kaooLiEB19dzUtpOxl7UgJ/mjiMti00dVIkWinoG5m9ufnc8UI6GTtyuOvcvnx/XH89q0YkyinoG5HlO3O4/YV0Dh4t4onrhnPRyd2CLklE6oGCvpGYuzSTH7+ykoRWzXhlyhkM6q7xeJHGQkEf5UpKnf9+ax1PfbiF03p34PFrh9OxlRbrFmlMFPRR7HBBMXfPXsa7a/dw3ehkHrp0MHGaHy/S6Cjoo9SunKPc8twSNuzJ5ReXDuaG03vqeTUijZSCPgpl7PiCydPTKCgq5dmbRnF2/4SgSxKRACnoo8y/lmdx79+X07VNc2ZPTqVv59ZBlyQiAas26M2sBzAd6AqUAlPd/bEKbX4IXFfuPQcCCe6ebWbbgFygBCiuagUUOTHuzhMfbOaRt9YzMqU9T01KpUN806DLEpEIEM4ZfTFwr7tnmFlrIN3M3nH3NV82cPdHgEcAzOwbwPfdPbvce4x19/21Wbj8n8LiUh6Yu5K/p2dy2andefjKU2gWGxN0WSISIaoNenffDewOfZ9rZmuBRGBNFYdcA8yqtQrlmA4eLWLKjHQ+3XyAu7/ej3vG9dNFVxH5DzUaozezFGAYsKiK/S2B8cCd5TY78LaZOfCUu089vlKlol05R7np2cVs3X+YR68ayhUjkoIuSUQiUNhBb2atgFeAe9z9UBXNvgF8UmHYZoy7Z5lZZ+AdM1vn7gsqef/JwGSA5OTksDvQWK3JOsRNzy3mSEEJz980ijP6dgq6JBGJUGHdPWNmcZSF/IvuPucYTSdSYdjG3bNC/90LzAVGVXagu09191R3T01I0HTAY/l4436ufuozmpjx9ymnK+RF5JiqDXorG/CdBqx19z8co11b4Gzgn+W2xYcu4GJm8cD5wKoTLboxm7s0kxufXUxS+xbM+c4Zeoa8iFQrnKGbMcAkYKWZLQtt+ymQDODuT4a2XQ687e6Hyx3bBZgbujgYC8x09zdroe5GaeqCzfz2jXWc3rsjT90wgjbN9Qx5EaleOLNuPgaqncbh7s8Bz1XYtgUYepy1SUhpqfPbN9by9MdbufiUbvzh6qGaPikiYdOdsRGuqKSUH/59Oa8uy+LGM1J48JJBWihERGpEQR/BjhaWMOXFdD5Yv48fXnAS3zmnj+bIi0iNKegj1MEjRdzy/BLSd3zB7755MteM0pRTETk+CvoItPdQPjc8s5jN+/J4/Fot+SciJ0ZBH2F2Zh/h+mmL2JdbwDM3juTMfrqnQEROjII+gmzel8f1Ty/icEExM24dzfDk9kGXJCJRQEEfIdZkHWLStEWYwezJp2vxbhGpNQr6CLBsZw43TFtEq2axzLh1NL0TWgVdkohEEQV9wJZsy+amZ5fQIb4pM28bTVL7lkGXJCJRRkEfoE827efW59Po3q45L956Gl3bNg+6JBGJQgr6gHy4YR+Tp6fRq1M8L9wymoTWzYIuSUSilII+AO+v28vtM9Lpm9CKGbeO1tquIlKnFPT17L21e5gyI4P+XVsx45bRtGupkBeRuhXWwiNSO95ds4c7ZqQzsFtrXrzlNIW8iNQLndHXk/fW7mHKi+kM6taG6beMpm0LPUteROqHzujrwfvr9jJlRgYDFfIiEgAFfR1bsGEft7+QTv+urXjhZoW8iNQ/BX0d+nTzfm6bnkafzmUXXtu2VMiLSP0LZ3HwHmb2vpmtNbPVZnZ3JW3OMbODZrYs9PVguX3jzWy9mW0ys/truwORasm2bG55Lo2eHVsy45ZRuvAqIoEJ52JsMXCvu2eYWWsg3czecfc1Fdp95O6XlN9gZjHA48B5QCawxMzmVXJsVFmRmcNNzy6hW9uyO147ttLNUCISnGrP6N19t7tnhL7PBdYCiWG+/yhgk7tvcfdCYDYw4XiLbQjWfX6IG55ZTPv4OF68TXe8ikjwajRGb2YpwDBgUSW7Tzez5WY238wGh7YlAjvLtcmkil8SZjbZzNLMLG3fvn01KStibN1/mOufXkzz2Bhm3noa3dq2CLokEZHwg97MWgGvAPe4+6EKuzOAnu4+FPgL8OqXh1XyVl7Z+7v7VHdPdffUhISGt6pSVs5Rrn96Ee7OjFtH06ODnkIpIpEhrKA3szjKQv5Fd59Tcb+7H3L3vND3bwBxZtaJsjP4HuWaJgFZJ1x1hDmQV8D10xZx6GgRz988ir6d9Tx5EYkc4cy6MWAasNbd/1BFm66hdpjZqND7HgCWAP3MrJeZNQUmAvNqq/hIkJtfxLefXUxWzlGeuWkkQxLbBl2SiMh/CGfWzRhgErDSzJaFtv0USAZw9yeBK4EpZlYMHAUmursDxWZ2J/AWEAM84+6ra7cLwckvKuG26Wms253L/9yQysiUDkGXJCLyFdUGvbt/TOVj7eXb/BX4axX73gDeOK7qIlhJqXP37KUs3JLNn751KmMHdA66JBGRSunO2OPg7vzs1VW8tXoPD14yiMuGhTvbVESk/inoj8Of3t3IrMU7mHJOH27+Wq+gyxEROSYFfQ3NWryDx97byJUjkvjRBScFXY6ISLUU9DXw3to9PDB3JeeclMDvvnkyoYlGIiIRTUEfpuU7c7hz5lIGd2/L49cOJy5Gf3Qi0jAorcKwM/sItzy/hI6tmvLMjSOJb6aFuUSk4VBiVSPnSCHffnYxRSXO7Mmj9JAyEWlwdEZ/DIXFpdz+QjqZ2UeZOmmEHm0gIg2Szuir4O7cP2cFi7Zm89jEUxndu2PQJYmIHBed0VfhiQ82MydjF98f158Jp+qGKBFpuBT0lXhj5W4eeWs9lw9L5Htf7xt0OSIiJ0RBX8GKzBx+8PIyRvRsz++v0Fx5EWn4FPTlfH4wn9ump9ExvhlPTRpBs9iYoEsSETlhCvqQ/KISJr+QRl5+MdNuTKWTFvQWkSihWTeEZti8soKVuw4ydVIqA7q2CbokEZFaozN6YOqCLby6LIt7z+vPeYO6BF2OiEitavRB/+GGfTz85jouPrkb3x2rGTYiEn0addBvP3CYu2Zm0L9Lax656hTNsBGRqBTO4uA9zOx9M1trZqvN7O5K2lxnZitCX5+a2dBy+7aZ2UozW2ZmabXdgeN1pLCY219Ix8yYOimVlk11uUJEolM46VYM3OvuGWbWGkg3s3fcfU25NluBs939CzO7EJgKjC63f6y776+9sk+Mu/Ojf6xgw55cnrtpFMkdWwZdkohInan2jN7dd7t7Ruj7XGAtkFihzafu/kXo5UIgqbYLrU3TPt7Kayt2c98FJ3FW/4SgyxERqVM1GqM3sxRgGLDoGM1uAeaXe+3A22aWbmaTj/Hek80szczS9u3bV5OyamTx1mx+N38d5w/qwpSz+9TZzxERiRRhD0ybWSvgFeAedz9URZuxlAX918ptHuPuWWbWGXjHzNa5+4KKx7r7VMqGfEhNTfUa9CFse3PzuXNmBskdWvL/rh6qi68i0iiEdUZvZnGUhfyL7j6nijanAE8DE9z9wJfb3T0r9N+9wFxg1IkWfTyKS0r53qylHMov4m/XD6dN87ggyhARqXfhzLoxYBqw1t3/UEWbZGAOMMndN5TbHh+6gIuZxQPnA6tqo/Ca+sM7G1i4JZvfXHay7nwVkUYlnKGbMcAkYKWZLQtt+ymQDODuTwIPAh2BJ0LDIcXungp0AeaGtsUCM939zdrsQDjeX7+XJz7YzLdSe3DFiIi+TiwiUuuqDXp3/xg45mC2u98K3FrJ9i3A0K8eUX92HzzKD15axoCurfnFhMFBliIiEoiovjO2uKSUu2cto6C4lMevG07zOD12WEQan6i+HfSx9zayeFs2f/zWUPokaGFvEWmcovaM/tNN+/nr+5u4akQSlw/TuLyINF5RGfTZhwu556Vl9OoUr3F5EWn0oi7oy55js5ycI0X85ZpheliZiDR6URf0MxZu5921e/nxhQMY3L1t0OWIiAQuqoJ+455cfv36Ws7un8DNY1KCLkdEJCJETdAXFJfwvdnLiG8Wq0VERETKiZoB7OISZ1C3Ntx7Xn86t24edDkiIhEjaoI+vlksj14d6E24IiIRKWqGbkREpHIKehGRKKegFxGJcgp6EZEop6AXEYlyCnoRkSinoBcRiXIKehGRKGfuHnQNX2Fm+4DtVezuBOyvx3KC0hj6qT5Gj8bQz0jvY093T6hsR0QG/bGYWVpo4fGo1hj6qT5Gj8bQz4bcRw3diIhEOQW9iEiUa4hBPzXoAupJY+in+hg9GkM/G2wfG9wYvYiI1ExDPKMXEZEaUNCLiES5QIPezMab2Xoz22Rm91ey38zsz6H9K8xseHXHmtlVZrbazErNLCKmQtVRPx8xs3Wh9nPNrF09dadSddTHX4XaLjOzt82se331pyp10c9y++8zMzezTnXdj2Opo8/yITPbFfosl5nZRfXVn6rU1WdpZneF9q02s/+uj75Uy90D+QJigM1Ab6ApsBwYVKHNRcB8wIDTgEXVHQsMBE4CPgBSg+pfPfTzfCA29P3DwMNR2Mc25Y7/HvBkNH6Wof09gLcou1GwU7T1EXgIuC/Iz6+e+jkWeBdoFnrdOei+unugZ/SjgE3uvsXdC4HZwIQKbSYA073MQqCdmXU71rHuvtbd19dfN6pVV/18292LQ8cvBJLqozNVqKs+Hip3fDwQ9MyBOulnyB+BHxHdfYwkddXPKcDv3b0AwN331kdnqhNk0CcCO8u9zgxtC6dNOMdGivro582UnXkEpc76aGa/MbOdwHXAg7VY8/Gok36a2aXALndfXtsFH4e6/Pt6Z2gI5Bkza197JR+Xuupnf+BMM1tkZh+a2charfo4BRn0Vsm2imczVbUJ59hIUaf9NLMHgGLgxeOqrnbUWR/d/QF370FZ/+487gprR63308xaAg8Q/C+xL9XVZ/k3oA9wKrAbePQ466stddXPWKA9ZUM9PwReNrPK2terIIM+k7JxyS8lAVlhtgnn2EhRZ/00s28DlwDXeWhAMCD18VnOBK444UpPTF30sw/QC1huZttC2zPMrGutVh6+Ovks3X2Pu5e4eynwP5QNfwSprv7OZgJzQsM9i4FSyh6GFqygLg5Q9ptvC2V/yb+8oDG4QpuL+c+LIYtrcOwHRMbF2DrpJzAeWAMkRHEf+5U7/i7gH9HYzwrHbyPYi7F19Vl2K3f894HZ0fhZAncAvwx935+yIR4Lsq/uHlzQh/4gLgI2UHYF+4Fyf1B3hL434PHQ/pWUC+7Kjg1tv5yy36oFwB7grcD/kOumn5tCf4mWhb6CnpFSF318BVgFrAD+BSRG42dZ4f23EWDQ1+Fn+UKo7QpgHuWCP8r62RSYEfp7mwGcG3Q/3V2PQBARiXa6M1ZEJMop6EVEopyCXkQkyinoRUSinIJeRCTKKehFRKKcgl5EJMr9fzrDRBsO1qn5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rs_tests = np.linspace(2, 20, 50)\n",
"gains = np.zeros(rs_tests.shape[0])\n",
"rs_i = np.zeros(rs_tests.shape[0])\n",
"for i, rs_t in enumerate(rs_tests):\n",
" rs.resistance = rs_t@u_kOhm\n",
" ta = simulator.transient(step_time=1@u_us, end_time=10000@u_us)\n",
" gains[i] = float(np.std(ta['1']-ta['2'])/np.std(ta['in1']-ta['in2']))\n",
" rs_i[i] = float(np.mean(ta['5'])/rs.resistance)\n",
"\n",
"plt.plot(rs_i, gains)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "95fa7e42",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb5ceccfc10>]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(rs_tests, rs_i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f59c7a4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}